• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.8 no.5
• /
• pp.367-372
• /
• 2015

In this study, locality of scleroderma was detected. Diagnostic method is difficult for scleroderma (skin curing; Scleroderma), and it is done by comparing the images of the normal subjects to the scleroderma patients, after performing monochrome processing. The saturation, brightness, and contrast are adjusted, and they were converted by using the process of Well Filter. As a result, the images were able to be used to clearly distinguish the symptoms of scleroderma. In addition, in a video of a healthy person, the line of sight of the observation given the image of scleroderma patients above sea level of height as $0^{\circ}$ is to implement the closing process to the rear Well Filter even only in so that the horizontal plane, and out at intervals of graph the amplitude difference of the video have I asked. The diagnostic criteria were determined for the healthy subjects and the scleroderma patients.

• Journal of Advanced Navigation Technology
• /
• v.16 no.1
• /
• pp.151-157
• /
• 2012

Recently, owing to the technology advances in computer science and image processing, age of face classification have become prevalent topics. It is difficult to estimate age of facial shape with statistical figures because facial shape of the person should change due to not only biological gene but also personal habits. In this paper, we proposed a robust age of face classification method by using Gabor feature and fuzzy support vector machine(SVM). Gabor wavelet function is used for extracting facial feature vector and in order to solve the intrinsic age ambiguity problem, a fuzzy support vector machine(FSVM) is introduced. By utilizing the FSVM age membership functions is defined. Some experiments have conducted to testify the proposed approach and experimental results showed that the proposed method can achieve better age of face classification precision.

• Journal of Broadcast Engineering
• /
• v.25 no.5
• /
• pp.776-788
• /
• 2020

In this paper, a vehicle type recognition system using deep learning and a license plate recognition system are proposed. In the existing system, the number plate area extraction through image processing and the character recognition method using DNN were used. These systems have the problem of declining recognition rates as the environment changes. Therefore, the proposed system used the one-stage object detection method YOLO v3, focusing on real-time detection and decreasing accuracy due to environmental changes, enabling real-time vehicle type and license plate character recognition with one RGB camera. Training data consists of actual data for vehicle type recognition and license plate area detection, and synthetic data for license plate character recognition. The accuracy of each module was 96.39% for detection of car model, 99.94% for detection of license plates, and 79.06% for recognition of license plates. In addition, accuracy was measured using YOLO v3 tiny, a lightweight network of YOLO v3.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2007.10a
• /
• pp.427-430
• /
• 2007

Many victims and property damages are caused in fires every year. In this paper, flame and smoke detection algorithm by using image processing technique is proposed to early alarm fires. The first decision of proposed algorithms is to check candidate of flame region with its unique color distribution distinguished from artificial lights. If it is not a flame region then we can check to candidate of smoke region by measuring difference of brightness and chroma at present frame. If we just check flame and smoke with only simple brightness and hue, we will occasionally get false alarms. Therefore we also use motion information about candidate of flame and smoke regions. Finally, to determine the flame after motion detection, activity information is used. And in order to determine the smoke, edges detection method is adopted. As a result of simulation with real CCTV video signal, it is shown that the proposed algorithm is useful for early fire recognition.

• Journal of Biomedical Engineering Research
• /
• v.41 no.5
• /
• pp.195-202
• /
• 2020

Needle detection in ultrasound images is sometimes difficult due to obstruction of fat tissues. Accurate needle detection using continuous ultrasound (CUS) images is a vital stage of treatment planning for tissue biopsy and brachytherapy. The main goal of the study is classified into two categories. First, new detection model, i.e. D-Attention Unet, is developed by combining the context information of 3D medical data and CUS images. Second, the D-Attention Unet model was compared with other models to verify its usefulness for needle detection in continuous ultrasound images. The continuous needle images taken with ultrasonic waves were converted into still images for dataset to evaluate the performance of the D-Attention Unet. The dataset was used for training and testing. Based on the results, the proposed D-Attention Unet model showed the better performance than other 3 models (Unet, D-Unet and Attention Unet), with Dice Similarity Coefficient (DSC), Recall and Precision at 71.9%, 70.6% and 73.7%, respectively. In conclusion, the D-Attention Unet model provides accurate needle detection for US-guided biopsy or brachytherapy, facilitating the clinical workflow. Especially, this kind of research is enthusiastically being performed on how to add image processing techniques to learning techniques. Thus, the proposed method is applied in this manner, it will be more effective technique than before.

• Proceedings of the Korean Society for Technology of Plasticity Conference
• /
• 2002.02a
• /
• pp.285-291
• /
• 2002

The shearing process for the sheet metal is normally used in the precision elements such as a frame of TFT-LCD or lead frame of If chips. In these precision elements, the burr formation prevents the system assembly and needs the additional burr removing process. In this paper, we developed the small size NC punching system which has an aligning kinematics between the rectangular shaped punch and die. The punch is driven by an ai cylinder and the sheet metal is moving on the X-Y table system which is driven by two stewing motors. The microprocessor control the whole system and communicate with the monitoring PC by RS232C serial communication protocol. The graphic user interface program in PC monitors nil control the punching system. The cross shaped joint hinge supports the punching die and positioned by two differential screws, whose are installed in perpendicular directions. The aligning between the punch and die is performed using the sheets of half thickness(0.1mm Brass) of the real process for the frame of the TFT-LCD. Using half thickness Brass, the burr formation is magnified and we can decide the aligning direction more easily then using the real thickness(0.2mm) Aluminum. In this paper, the aligning results are measured manually using the SEM photographs and we hope to make the automated aligning procedures using some kinds of image processing techniques.

• Journal of Sensor Science and Technology
• /
• v.14 no.4
• /
• pp.272-277
• /
• 2005

A CMOS active pixel sensor has been designed and fabricated using standard 2-poly and 4-metal $0.35{\mu}m$ CMOS processing technology. The CMOS active pixel sensor has been made up of a unit pixel having a highly sensitive PMOSFET photo-detector and electronic shutters that can control the light exposure time to the PMOSFET photo-detector, correlated-double sampling (CDS) circuits, and an 8-bit two-step flash analog to digital converter (ADC) for digital output. This sensor can obtain a stable photo signal in a wide range of light intensity. It can be realized with a special function of an electronic shutter which controls the light exposure-time in the pixel. Moreover, this sensor had obtained the digital output using an embedded ADC for the system integration. The designed and fabricated image sensor has been implemented as a $128{\times}128$ pixel array. The area of the unit pixel is $7.60{\mu}m{\times}7.85{\mu}m$ and its fill factor is about 35 %.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.10
• /
• pp.3955-3971
• /
• 2020

Due to the significant increase in the use of autonomous car technology, it is essential to integrate this technology with high-precision digital map data containing more precise and accurate roadway information, as compared to existing conventional map resources, to ensure the safety of self-driving operations. While existing map technologies may assist vehicles in identifying their locations via Global Positioning System, it is however difficult to update the environmental changes of roadways in these maps. Roadway vision algorithms can be useful for building autonomous vehicles that can avoid accidents and detect real-time location changes. We incorporate a hybrid architectural design that combines unsupervised classification of vision data with supervised joint fusion classification to achieve a better noise-resistant algorithm. We identify, via a deep learning approach, an intelligent hybrid fusion algorithm for fusing multimodal vision feature data for roadway classifications and characterize its improvement in accuracy over unsupervised identifications using image processing and supervised vision classifiers. We analyzed over 93,000 vision frame data collected from a test vehicle in real roadways. The performance indicators of the proposed hybrid fusion algorithm are successfully evaluated for the generation of roadway digital maps for autonomous vehicles, with a recall of 0.94, precision of 0.96, and accuracy of 0.92.

• Journal of Digital Convergence
• /
• v.14 no.11
• /
• pp.319-324
• /
• 2016

In the case of robot and Argument Reality applications using a camera in environments, a technology to estimate planes is a very important technology. A RGB-D camera can get a three-dimensional measurement data even in a flat which has no information of the texture of the plane;, however, there is an enormous amount of computation in order to process the point-cloud data of the image. Furthermore, it could not know the number of planes that are currently observed as an advance, also, there is an additional operation required to estimate a three dimensional plane. In this paper, we proposed the real-time method that decides the number of planes automatically and estimates the three dimensional plane by using the continuous data of an RGB-D camera. As experimental results, the proposed method showed an improvement of approximately 22 times faster speed compared to processing the entire data.

• Korean Journal of Food Science and Technology
• /
• v.27 no.4
• /
• pp.525-531
• /
• 1995

Three quality factors of bread during baking process were measured to develop neural network models for bread baking process. Firstly, volume and browning changes during bread baking process were measured using image processing technique and temperature changes inside the bread during process were measured by K-type thermocouples. Relationships among them showed nonlinearity. Secondly, multilayer perception structure with error back propagation learning was used to construct neural network models. Three neural network models for volume, browning, and bread temperature were developed respectively. Developed models showed good performance with predictive error of 4.62% for volume and browning changes after 30 seconds, 7.38% for volume and browning changes after 2 minutes, and 1.09% for temperature change inside the bread respectively.